Donald Clark Plan B

What is Plan B? Not Plan A!

Tuesday, January 23, 2018

8 ways Africa can use AI to leapfrog into the future

Africa is huge. Just how huge is rarely appreciated but this map helps. This massive landmass makes land transport difficult, physical internet cabling difficult, infrastructure difficult. But with two spots from one satellite, it is possible to cover the entire continent. Bad or non-exiting infrastructure is the condition for leapfrogging.

So here's a question.... What African leapfrogged the transport and energy sectors to
such a degree that the oil economy look as though it’s on the way out? He did
this by seeing the existing model as the problem – the oil economy - so created
the self-driving, actually AI-driven, car and panels/batteries that change the way we power homes,
even entire regions. He is, of course, Elon Musk, a leapfrogger. But Africa
leaps over frogs in all sorts of ways, from mobile banking to drones for blood
delivery.

The first technology, the stone axe, was invented by early
hominoidspecies in the rift valley in
Africa, that allowed us to leapfrog other species, who may have been stronger and faster, but lacked the technology to compete. The first writing in the Nile Valley, again in Africa, on the
first flexible writing material, Papyrus, also invented in Africa, allowed the
Egyptians to leapfrog other civilisations, a stable civilisation that lasted continuously
for 4000 years, longer than any other Empire ever. The very tools and technology that
the modern world is built on were first seen in Africa.

There’s a lesson here – the ‘Leapfrog
Principle’. This is the idea that one can innovate in environments where
precedents and incumbents are poor, primitive or absent, easier than in wealthier
or technologically richer environments. Africa can, again, be the crucible for
leapfrog ideas and development. In finance, healthcare, energy, agriculture and
education, AI can augment and improve productivity.

Leapfrog 1 Mobile banking

Africa had little in the way of a retail banking
infrastructure and most people did not have a bank account. Along comes the
ubiquity of cheap mobile devices and Africa does what richer countries are only
now waking up to – mobile banking. In its wake came advantages in communications, finding work, paying bills and agricultural information - markets, teachniques and so on. The runaway success of M-Pesa, the mobile money
transfer service launched by Safaricom, Kenya’s largest mobile operator and
Vodafone, in 2007, has allowed millins to pay bills, buy goods, receive
remittances from abroad and even access learning. None of this would have been possible without AI-driven encryption and now AI as the new UI interfaces.

Leapfrog 2 Zipline

Take Zipline, in Rwanda, where drones deliver blood to rural
locations. Doctors request blood for ‘at risk’ patients and drones deliver,
dropping the protected packages by parachutes, from 30 feet, into the backyard
of the clinic, aided by GPS and navigational software. This is fast, cheap, efficient and saves lives. Why Rwanda? Well
the road infrastructure prohibits speed of delivery, there is less regulation
to hold back these innovations and, as a small country, it is ambitious and
willing to take more risks. Older countries tend to become more risk averse. Strangely
enough it is sometimes the absence of physical infrastructure; roads, fixed
line telephone networks, transport options, power stations, oil reserves, that
make leapfrogging more likely. The investment in leapfrog technology has less
competitive pressure from incumbent technology and infrastructure.

Leapfrog 3Offgrid Electric

The International Energy Agency states that there are over 600
million people in sub-Saharan Africa that do not have access to electricity. An
African startup, Off Grid Electric, backed by Solar City, wants to rack up the
supply of solar panels across Africa, with at an affordable charge of $7 a
month for the system. It already powers 125,000 households. Musk has taken
technology built for the wealthy car industry and applies it in a modular, LED,
robust, affordable way to an African problem – no infrastructure and low
income. The project has the possibility of scale and sustainability to the 1.3
billion people globally who lack access to affordable electricity. In the
continent of sunshine, solar leapfrogs other forms of energy supply.

Leapfrog 4 Algorithmic agriculture

The perfect
storm of satellites or drones with analytics of water, wind damage, soil
condition, temperature and so on, even predictive software may lead to step
changes in productivity. Precision agriculture turns AI into real solutions, in
everything from GPS guidance, control systems, sensors, robotics,
drones, autonomous vehicles, GPS-based soil sampling and so on. Getting the
most out of every centimetre of land, sensors for yield prediction, scanning
for disease and damage, productivity gains are there for the taking. In agriculture,
data can fed software to increase yields to feed people.

Leapfrog 5 Entertainment

This one's often forgotten but African's love music and, arguably, gave us the Blues, Jazz and what became Rock 'n Roll, even Rap. Mobiles deliver everything from ringtones to radio. I remember an NGO worker in Uganda telling me that whenever they tried to use devices or flash drives in education, they were co-opted for music! Bt this goes well beyond music with services for photo-sharing and movie distribution. What's interesting here is the way African needs have forced the likes of Netflix to develop new AI-driven compression techniques for delivery in low bandwidth environments

Leapfrog 6Investment

Leapfrog Investments is an investment company that
specialises in African and Asian investments. This matters, as growth needs an
ecosystem for sustainable success. It needs risk capital to make those leaps,
not of faith, but of assessed risk. Others include Carlyle, TPG and Abraaj.
Unfortunately, start-up finance in Africa is paltry at less than $150 million. Africa
is going through a population explosion with young, tech savvy populations that
are used to mobile solutions. We need to harness their energy and talents.

Leapfrog 7 Starnet
Musk is buiding a Starnet system which is a satellite system that delivers high bandwidth internet to the world, especially sparsely pupulated areas. This promises to accelerate progress in Africa. An African will have come full circle and delivered what the continent needs to do the things it wants.

Leapfrog 8 Education

Let’s apply this principle to learning. The current problem
in Africa is poor schooling, and the need for vocational skills, along with
sensitivity to local languages. This is where AI comes in. Think of these two
letters as the hind legs that allow the frogs to leap. OK, I know this metaphor
is being stretched a little but bear with me…. One of the barriers to leapfrogging is
education. To escape from the trap of poverty, one needs vision, confidence and
competence. Rather than rely on foreign workers to provide practical skills in
building and tourism, we must focus on vocational skills. It is pointless
investing in higher education when there is no middle ground. This must happen
at school and college level. Africa is going through a population explosion
with young, tech savvy populations that are used to mobile solutions.

It is in
education that leapfrogging can have the greatest causal effect. AI can create
online learning cheaply (WildFire), and through AI assisted translation, create
such learning in multiple languages. AI can personalise learning through
adaptive systems. (CogBooks). This helps build a platform of knowledge delivery, so hat teachers can focus on skills. In the same way that blood type has to be
selected or every patient, learning needs to be delivered to each person in a
way that suits their needs – and the diversity or variability of these needs is
much greater in Africa, than in a developed country. This is not the primitive
Hole-in-the Wall or tablets parachuted into villages approach but scalable,
sustainable learning to help teachers teach and learners to learn.

Don’t dump devices in developing world. That’s not leapfrogging, it’s device dumping. Sugata
Mitra and Negroponte have both made a career out of dumping devices into the
developing world and teachers lap it up as if they’re some sort of saints.
Listen carefully – they don’t like teachers and schools. Researchers, like
Arora, from Erasmus University Rotterdam, “little real independent evidence,
other than that provided by HiWEL“, accusing Mitra of “not comparing
amount of time spent on hole-in-wall material with same time in school…
making the comparison meaningless”. It was, she concluded,“self-defeating…
‘hole-in-the-wall’ has become the ‘computer-in-the-school”. This was
confirmed by Mark Warschauer,Professor of Education at the
University of California, who also visited sites, only to find that “parents
thought the paucity of relevant content rendered it irrelevant“ and that “most
of the time they were playing games…. with low level learning and not
challenging”. The “internet rarely functioned” and “overall the
project was not very effective”. I also visited a site, in Africa, and
confirmed all of this and more. Read Mitra’s comment on my blog, “it took me
30 minutes to think about and write this response. I would have spent the time
on planning a new project for very poor children. Would someone, perhaps
Donald, like to take the responsibility for this wastage and the resultant loss
to them.” Sugata Mitra. This is what happens when devices trump reason.

Conclusion

But let’s not underestimate the problems. Corruption,
unstable political environments, poverty and weak regulatory environments don’t
encourage investment and sustainable growth. To leapfrog, one must have solid
ground from which to leap. Without a stable platform, these will be leaps of
faith or leaps into the darkness. Innovation is only innovation of it is
sustainable, that means stable regulations, a war on corruption and an
investment environment that supports staged growth. Let’s start with education.

Thursday, January 18, 2018

Nurses – why degrees are not the answer for NHS crisis

The graduate demand for nursing is a filter excluding many
who used to go into the profession. My mother was a nurse, my sister was a
nurse for 30 plus years – neither would now have got into the profession
because of the academic entrance demands. Working class youngsters, in particular, are
being excluded and I do not believe for one minute that they are unsuitable.
The nursing shortage is not only caused by this hurdle but it has been exacerbated
by the need for a someone who wants to be a nurse to get University entrance
qualifications, spend years at University, then exit into a relatively low paid
profession, with a huge loan.

Alan Ryan, who was a nurse for 20 years, and knows a thing
or two about training in the NHS, said something quite profound in Berlin
recently. “All of our jobs (in NHS) are, in
practice, apprenticehips, from consultants to cleaners”. His point was that
healthcare is an eminently, practical affair. He supports alternative routes into healthcare professions.

In truth Higher Education in the UK has land-grabbed
vocational education, mainly on the basis of increasing their revenues. What
were adequate, shorter and more experiential, training courses are now degrees,
making them longer and far more expensive, whether for the state or individual.

Universities may claim to be about critical thinking but a
glance at some of the degrees on offer show that this is far from the truth –
Dentists, Doctors, Nurses, Lawyers, Engineers and so on. In truth, as Roger
Schank tells us, Universities are about “creating
Scholars”, and, as he says “we have
enough Scholars already”. I’d add that there may be a surplus, as shown by
the ease at which adjuncts can be hired to do the ‘teaching’ even in top
Universities. This is not the environment into which nursing easily fits.

We have many nurses from other countries, even the EU, such
as Germany, who are hired without going through this University experience, so
it is not as if it is a necessary condition for success. Those who deliver such
courses will claim that a nurse’s job is more complex than it used to be and of
that, I have no doubt. But complex does not necessarily mean more lecturing and
theory. 'It is difficult to get a man to understand something, when his salary
depends on his not understanding it’ said Upton Sinclair, and it is difficult
to get something out of the world of lectures and essays once ‘Lecturers’ get a
hold of it.

There are many causes to hte current nursing crisis:

failure to plan for demand

degree course entrance qualifications

abolishing bursaries

new English tests

agency costs

foreign country demands

working conditions

But part of the solution here is to reverse this policy of Nursing
degrees, not by demolishing that option but opening up alternative routes,
especially apprenticeships. A vocational route is badly needed, and should have
been opened up years ago. In practice, we depended on migrant labour and extortionate
agency fees. We didn’t have to pay for their education, which is neither good
for us or the countries from which they came, but that is not a good excuse for
the failure to train our own nurses. Brexit will at least slow that process bu we need an alternative route. The nursing assistant route is a start - we need much, much more.

Tuesday, January 16, 2018

AI just outperformed humans at reading, potentially putting millions of customer service jobs at risk of automation. Could it do the same in learning?

Something
momentous just happened. An AI programme, from Alibaba, can now, for the first time, read a text and
understand it better than humans. The purple line has just crossed the red line and the implications are huge.

Think through the consequences here, as this software, using NLP and machine learning,
gets better ad better. The aim is to provide answers to questions. This is
exactly what millions of people do in jobs around the world. Customer service
in call centres, Doctors with patients, anywhere people reply to queries... and any interactions where language
and its interpretation matter.

Health warning

First we
must be careful with these results, as it depends on two things 1) the nature
of the text 2) what we mean by ‘reading’. Such approaches often work well with factual texts but not with more complex and subtle texts, such as
fiction, where the language is difficult to parse and understand, and where
there is a huge amount of ‘reading between the lines”. Think about how
difficult it is to understand even that last sentence. Nevertheless, this is a
breakthrough.

The Test
It is the first time a machine has out-done a real person in
such a contest.They used the Stanford Question
Answering Dataset, to assess reading comprehension. The test is to provide exact
answers to more than 100,000 questions. As an open test environment, you can do
it yourself, which makes the evidence and results transparent. Alibaba’s neural
network model, based on a Hierarchical Attention Network, which reads down
through paragraphs to sentences to words, identifies potential answers and their
probabilities. Alibaba has already used this technology in their customer
service chatbot, Dian Xiaomi, to an average of 3.5 million customers a day on the
Taobao and Tmall platforms. (10 uses for chatbots in learning).

Learning

Indeed, the
one area that is likely to benefit hugely from these advances is education and
training. The Stanford dataset does have questions that are logically complex and, in terms of domain, quite obscure, but one should see this development as great at knowledge but not yet effective with questions beyond this. That’s fine as there is much that can be achieved in learning.We have been using this AI approach to create online learning content, in minutes not months, through WildFire. Using asimilar approach, we identify the main learning
points in any document, PPT or video, and build online learning courses quickly, with an
approach based on recent cognitive psychology that focuses on retention. In
addition, we add curated content.

Pedagogy

The online
learning is very different from the graphics plus multiple-choice paradigm. Rather
than rely on the weak ‘select from a list’ MCQs (see critique here), we get learners to enter their
answers in context. It focuses on open-input and retention techniques outlined
by Roedinger and McDaniel in Make It
Stick.

Speed

To give you
some idea of the sheer speed of this process we recently completed 158 modules
for a global company, literally in days, without a single face-to-face meeting
with the project manager. The content was then loaded up to their LMS and is ready to
roll. This was good content and they are very happy with the results.

Pain relief

An
interesting outcome of this approach to creating content was the lack of heat generated during the
production process. There was no SME/designer friction, as that was automated.
That’s one of the reasons we didn’t need a single face-to-face meeting. It allowed
us to focus on getting it done and quality control.

Sectors

Organisations
have been using this AI-created content as pre-training for face-to-face
training for auditors in Finance, product knowledge and GMP in Manufacturing, health and
safety, everything from nurse training to clinical guidelines in the NHS,
apprenticeships in a global Hospitality company. All sorts of education and
training in all sorts of contexts.

Conclusion

The breakthrough saw Microsoft and Baidu perform similarly, showing that the new AI-war is between China and the US. That’s a shame but we still have some edge here in Europe and the UK, if we could only overcome our tendency to see AI as a dystopian entity and start to use this stuff for social good, rather than being obsessed with ill-informed critiques. If we don’t, they will. These AI
techniques have already hit the learning market. It is already automating the
production of learning in that huge motherload of education and training: 101
courses and topics such as compliance, process, procedures, product knowledge
and so on. Beyond this, AI-driven curation, which we use to supplement the core
courses is also possible. If you want see how AI and WildFire can help you
create content quickly, at much lower cost and increase retention, drop us a line and we’ll arrange a demo.

Monday, January 08, 2018

We clearly have a productivity problem in manufacturing, in
part due to a lack of training and skills. As manufacturing becomes more
complex and automated, it needs lots of skills other than those traditionally
repetitive jobs that are being replaced. Could AI help solve this problem? AI may lead to a loss of jobs
but we’re showing that AI can also help train in what jobs there are to
increase productivity and help in training for new jobs. We’ve been creating
online learning quickly and at low cost through WildFire.

Productivity puzzle

The
manufacturing sector continues to struggle for productivity, despite growing
levels of economic activity. Manufacturing productivity actually fell by 0.2
per cent in the third quarter of 2016, compared to 0.3 per cent growth in
services. Many attribute this, at least partially, to low skills and training. As
productivity growth seems to have stalled, technology offers a reboot, both in
process and learning. Typically ‘basic goods’ manufacturing has been stuck with
the rather basic use of technology. This is in stark contrast to ‘advanced
manufacturing’ which has been eager to adopt advanced technology. Both,
however, have been tardy in their use of technology to get knowledge and skills
to their staff. They have both been far behind those in finance, healthcare,
hospitality and other sectors. Understandably, learning in manufacturing has
been largely classroom and learning by doing. Yet, as manufacturing becomes more
complex, knowledge and skills has become ever more important.

Double-dividend

One
immediate way to increase productivity is through online learning. This has a
double-dividend, in that it can save costs (travel, rooms, equipment and
trainers) as well as increase productivity through better knowledge and skills.
With access to mobile technology, learning can be delivered to distributed
audience, even on the shop-floor. In addition, shift work and access to
training in down-time and gaps in production, can also be achieved.

Barriers

Manufacturing is often thought of as a sector not much
involved in online learning. Several factors are at work here.

1. Lots of SMEs without large training budgets

2. Less likely to find a LMS to deliver content

3. Less likely to find L&D aware of online learning

3. Less access to devices for online learning

4. Practical environment where factory floor training more
prevalent.

To make online learning work there needs to be more
awareness of why online learning can help as well as how it can be done.

What we did

First we focused on basic, generic training needs, and
produced dozens of modules on:

1. Manual handling

2. Health and safety

3. General Manufacturing Practice

4. Language of manufacturing

5. Gas Cylinders

6. Product knowledge

These are largely knowledge-based modules that underpin
practical training in the lab, workshop or factory floor. Bringing everyone up
to a common standard really helps when it comes to practical, vocational
training. You really should understand what is going on with the science of gas
storage and use if you handle dangerous gases and want to weld safely. In addition we trained everyone from apprentices and administration staff to sales people.

To this end we produced modules quickly and cheaply using
WildFire, an AI service that takes any document, PowerPoint or video, and
creates online learning in minutes not months. We have done this successfully
in finance and healthcare but manufacturing posed different challenges.

1. Much of the training is text heavy from manuals without
any sophisticated use of images. That we solved through quick and low cost
photo-shoots. Literally shooting to a shot list as the online modules had
already been created.

2. In not one case did we find a LMS (Learning management
System), so we had to deliver from the WildFire server. This actually has one
great advantage in that it freed us from the limitations of SCORM. We could
gather oodles of data for monitoring and analysis.

3. Doing this learning at any time allows learners to train
in down time or at anytime 24/7.

4. It means consistency.

5. We could deliver to any service, especially mobile, which
helped.

Conclusion

We are still delivering and analysing the
results. Sure there have been issues, especially in the absence of L&D
staff in the target organisations but when it works, it works beautifully. If
we are to take productivity seriously in the UK we must realise that this means
better training and therefore performance. Wouldn’t it be wonderful if AI helps
increase productivity through online learning so that people can skill
themselves into relevant employment? AI may automate parts of roles but it can
also be used to skill for the newly created roles. If you want to find out more
please inquire here.